journal articles
TOWARDS AN AI BIOMEDICAL SCIENTIST: ACCELERATING DISCOVERIES IN NEURODEGENERATIVE DISEASE
Kaleigh F. Roberts, Eric C. Landsness, Justin Reese, Donald Elbert, Gabrielle Strobel, Elizabeth Wu, Yixin Chen, Albert Lai, Zachary B. Abrams, Mingfang Zhu, Justin Melendez, Srinivas Koutarapu, Sihui Song, Yun Chen, Robert Lazar, Payam Barnaghi, John F. Crary, Sergio Pablo Sardi, Marc D. Voss, Rajaraman Krishnan, Joel W. Schwartz, Ron Mallon, Gustavo A. Jimenez-Maggiora, Chenguang Wang, Thomas Sandmann, Niranjan Bose, Mukta Phatak, Gayle Wittenberg, Yannis G. Kevrekidis, Cassie S. Mitchell, Ludovico Mitchener, Cassie S. Mitchell, Ludovico Mitchener, Towfique Raj, Luca Foschini, Gregory J. Moore, Randall J. Bateman
J Prev Alz Dis 2026;1(13)
Despite major advances in Alzheimer’s disease and related diseases (ADRD) research, the translation of discoveries into impactful clinical interventions remains slow. Overwhelming data complexity, fragmented knowledge, and prolonged research cycles hinder progress in understanding and treating neurodegenerative diseases. Artificial intelligence (AI) offers a promising path forward, particularly when developed as a scientist-in-the-loop system that collaborates with researchers throughout the scientific discovery process. This paper introduces the concept of an AI Biomedical Scientist, an intelligent platform designed to support literature synthesis, hypothesis generation, experimental design, and data interpretation. This platform aims to function as a holistic scientific partner, integrating diverse biomedical data and expert reasoning to accelerate discovery. We review commercial and academic efforts and introduce targeted Minimum Viable Products (MVPs) needed for general biomedical research lab utilization of AI, such as robust and accurate tools for literature and data analysis, negative data models, and virtual peer review, with a longer-term vision of foundation models trained directly on biomedical datasets. In AD and neurodegeneration research, such tools are anticipated to deliver efficiency gains ranging from modest improvements in specific research tasks to potential multi-fold accelerations in discovery workflows as systems mature and scale. This review examines the technical foundations, challenges, and anticipated impacts of AI and aims to inform and engage researchers in utilizing these systems to transform biomedical discovery, starting with AD and extending to other complex conditions.
CITATION:
Kaleigh F. Roberts ; Eric C. Landsness ; Justin Reese ; Donald Elbert ; Gabrielle Strobel ; Elizabeth Wu ; Yixin Chen ; Albert Lai ; Zachary B. Abrams ; Mingfang Zhu ; Justin Melendez ; Srinivas Koutarapu ; Sihui Song ; Yun Chen ; Robert Lazar ; Payam Barnaghi ; John F. Crary ; Sergio Pablo Sardi ; Marc D. Voss ; Rajaraman Krishnan ; Joel W. Schwartz ; Ron Mallon ; Gustavo A. Jimenez-Maggiora ; Chenguang Wang ; Thomas Sandmann ; Niranjan Bose ; Mukta Phatak ; Gayle Wittenberg ; Yannis G. Kevrekidis ; Cassie S. Mitchell ; Ludovico Mitchener ; Towfique Raj ; Luca Foschini ; Gregory J. Moore ; Randall J. Bateman (2025): Towards an AI biomedical scientist: Accelerating discoveries in neurodegenerative disease. The Journal of Prevention of Alzheimer’s Disease (JPAD). https://doi.org/10.1016/j.tjpad.2025.100398